Speaker identification using multimodal neural networks and wavelet analysis

Almaadeed, Noor; Aggoun, A.; Amira, Abbes

doi:10.1049/iet-bmt.2014.0011

Cited by 52 publications

(31 citation statements)

References 32 publications

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“…A number of researches employed neural network to facilitate their researches. Almaadeed et al introduced a wavelet analysis and neural networks based text-independent multimodal speaker identification system [4]. The wavelet analysis firstly employs wavelet transforms to execute feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model

Liu

Jing

2016

Computational Intelligence and Neuroscience

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Artificial Neural Network (ANN) is a widely used algorithm in pattern recognition, classification, and prediction fields. Among a number of neural networks, backpropagation neural network (BPNN) has become the most famous one due to its remarkable function approximation ability. However, a standard BPNN frequently employs a large number of sum and sigmoid calculations, which may result in low efficiency in dealing with large volume of data. Therefore to parallelize BPNN using distributed computing technologies is an effective way to improve the algorithm performance in terms of efficiency. However, traditional parallelization may lead to accuracy loss. Although several complements have been done, it is still difficult to find out a compromise between efficiency and precision. This paper presents a parallelized BPNN based on MapReduce computing model which supplies advanced features including fault tolerance, data replication, and load balancing. And also to improve the algorithm performance in terms of precision, this paper creates a cascading model based classification approach, which helps to refine the classification results. The experimental results indicate that the presented parallelized BPNN is able to offer high efficiency whilst maintaining excellent precision in enabling large-scale machine learning.

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Section: Related Workmentioning

confidence: 99%

Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model

Liu

Jing

2016

Computational Intelligence and Neuroscience

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“…Phoneme-related effects are reduced to increase speaker information of an utterance. Discrete wavelet transform, wavelet sub-band coding, and irregular decomposition techniques are gathered for feature extraction, and neural networks are used for classification in [55]. Compared with the conventional features and GMM-UBM approach, the proposed method gave the best recognition rate.…”

Section: Other Advancements In Feature Extractionmentioning

confidence: 99%

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

DİŞKEN

Tüfekçi

Sarıbulut

et al. 2016

IETE Technical Review

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Speech is a signal that includes speaker's emotion, characteristic specification, phonemeinformation etc. Various methods have been proposed for speaker recognition by extracting specifications of a given utterance. Among them, short-term cepstral features are used excessively in speech, and speaker recognition areas because of their low complexity, and high performance in controlled environments. On the other hand, their performances decrease dramatically under degraded conditions such as channel mismatch, additive noise, emotional variability, etc. In this paper, a literature review on speaker-specific information extraction from speech is presented by considering the latest studies offering solutions to the aforementioned problem. The studies are categorized in three groups considering their robustness against channel mismatch, additive noise, and other degradations such as vocal effort, emotion mismatch, etc. For a more understandable representation, they are also classified into two tables by utilizing their classification methods, and used data-sets.

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“…Such a comparison is carried out in [13]. The optimum number of MFCC features are important for speaker recognition accuracy [14]. In [14], comparison between MFCC, Delta MFCC and Delta-delta MFCC is carried to find optimum number of MFCC features.…”

Section: Related Workmentioning

confidence: 99%

Combining dynamic features with MFCC for text-independent speaker identification

Chaudhari

Rahulkar

Dhonde

2015

2015 International Conference on Information Processing (ICIP)

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This paper presents text-independent speaker identification scheme based on the combination of dynamic features with Mel Frequency Cepstral Coefficients (MFCC). First, MFCCs are used to extract the speaker specific features and then vector quantization is used to model the speaker. We compare the performance of speaker identification system using number of MFCC filters and centroids of vector quantization. In addition, we also compare the effect of possible combinations of MFCC with log energy, delta and double delta coefficients for number of filters and centroids of vector quantization on recognition rate. The experimental results have been evaluated on the developed database of 75 speakers.

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Speaker identification using multimodal neural networks and wavelet analysis

Cited by 52 publications

References 32 publications

Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model

Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

Combining dynamic features with MFCC for text-independent speaker identification

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